from pathlib import Path
import argparse

from . import colmap_from_nvm
from ... import extract_features, match_features, triangulation
from ... import pairs_from_covisibility, pairs_from_retrieval, localize_sfm


CONDITIONS = [
    "dawn",
    "dusk",
    "night",
    "night-rain",
    "overcast-summer",
    "overcast-winter",
    "rain",
    "snow",
    "sun",
]


def generate_query_list(dataset, image_dir, path):
    h, w = 1024, 1024
    intrinsics_filename = "intrinsics/{}_intrinsics.txt"
    cameras = {}
    for side in ["left", "right", "rear"]:
        with open(dataset / intrinsics_filename.format(side), "r") as f:
            fx = f.readline().split()[1]
            fy = f.readline().split()[1]
            cx = f.readline().split()[1]
            cy = f.readline().split()[1]
            assert fx == fy
            params = ["SIMPLE_RADIAL", w, h, fx, cx, cy, 0.0]
            cameras[side] = [str(p) for p in params]

    queries = sorted(image_dir.glob("**/*.jpg"))
    queries = [str(q.relative_to(image_dir.parents[0])) for q in queries]

    out = [[q] + cameras[Path(q).parent.name] for q in queries]
    with open(path, "w") as f:
        f.write("\n".join(map(" ".join, out)))


parser = argparse.ArgumentParser()
parser.add_argument(
    "--dataset",
    type=Path,
    default="datasets/robotcar",
    help="Path to the dataset, default: %(default)s",
)
parser.add_argument(
    "--outputs",
    type=Path,
    default="outputs/robotcar",
    help="Path to the output directory, default: %(default)s",
)
parser.add_argument(
    "--num_covis",
    type=int,
    default=20,
    help="Number of image pairs for SfM, default: %(default)s",
)
parser.add_argument(
    "--num_loc",
    type=int,
    default=20,
    help="Number of image pairs for loc, default: %(default)s",
)
args = parser.parse_args()

# Setup the paths
dataset = args.dataset
images = dataset / "images/"

outputs = args.outputs  # where everything will be saved
outputs.mkdir(exist_ok=True, parents=True)
query_list = outputs / "{condition}_queries_with_intrinsics.txt"
sift_sfm = outputs / "sfm_sift"
reference_sfm = outputs / "sfm_superpoint+superglue"
sfm_pairs = outputs / f"pairs-db-covis{args.num_covis}.txt"
loc_pairs = outputs / f"pairs-query-netvlad{args.num_loc}.txt"
results = (
    outputs / f"RobotCar_hloc_superpoint+superglue_netvlad{args.num_loc}.txt"
)

# pick one of the configurations for extraction and matching
retrieval_conf = extract_features.confs["netvlad"]
feature_conf = extract_features.confs["superpoint_aachen"]
matcher_conf = match_features.confs["superglue"]

for condition in CONDITIONS:
    generate_query_list(
        dataset, images / condition, str(query_list).format(condition=condition)
    )

features = extract_features.main(feature_conf, images, outputs, as_half=True)

colmap_from_nvm.main(
    dataset / "3D-models/all-merged/all.nvm",
    dataset / "3D-models/overcast-reference.db",
    sift_sfm,
)
pairs_from_covisibility.main(sift_sfm, sfm_pairs, num_matched=args.num_covis)
sfm_matches = match_features.main(
    matcher_conf, sfm_pairs, feature_conf["output"], outputs
)

triangulation.main(
    reference_sfm, sift_sfm, images, sfm_pairs, features, sfm_matches
)

global_descriptors = extract_features.main(retrieval_conf, images, outputs)
# TODO: do per location and per camera
pairs_from_retrieval.main(
    global_descriptors,
    loc_pairs,
    args.num_loc,
    query_prefix=CONDITIONS,
    db_model=reference_sfm,
)
loc_matches = match_features.main(
    matcher_conf, loc_pairs, feature_conf["output"], outputs
)

localize_sfm.main(
    reference_sfm,
    Path(str(query_list).format(condition="*")),
    loc_pairs,
    features,
    loc_matches,
    results,
    covisibility_clustering=False,
    prepend_camera_name=True,
)
